# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""A basic MNIST example using JAX together with the mini-libraries stax, for
neural network building, and optimizers, for first-order stochastic optimization.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import time
import itertools

import numpy.random as npr

import jax.numpy as np
from jax.config import config
from jax import jit, grad, random
from jax.experimental import optimizers
from jax.experimental import stax
from jax.experimental.stax import Dense, Relu, LogSoftmax
from examples import datasets


def loss(params, batch):
  inputs, targets = batch
  preds = predict(params, inputs)
  return -np.mean(preds * targets)

def accuracy(params, batch):
  inputs, targets = batch
  target_class = np.argmax(targets, axis=1)
  predicted_class = np.argmax(predict(params, inputs), axis=1)
  return np.mean(predicted_class == target_class)

init_random_params, predict = stax.serial(
    Dense(1024), Relu,
    Dense(1024), Relu,
    Dense(10), LogSoftmax)

if __name__ == "__main__":
  rng = random.PRNGKey(0)

  step_size = 0.001
  num_epochs = 3
  batch_size = 128
  momentum_mass = 0.9

  train_images, train_labels, test_images, test_labels = datasets.mnist()
  num_train = train_images.shape[0]
  num_complete_batches, leftover = divmod(num_train, batch_size)
  num_batches = num_complete_batches + bool(leftover)

  def data_stream():
    rng = npr.RandomState(0)
    while True:
      perm = rng.permutation(num_train)
      for i in range(num_batches):
        batch_idx = perm[i * batch_size:(i + 1) * batch_size]
        yield train_images[batch_idx], train_labels[batch_idx]
  batches = data_stream()

  opt_init, opt_update = optimizers.momentum(step_size, mass=momentum_mass)

  @jit
  def update(i, opt_state, batch):
    params = optimizers.get_params(opt_state)
    return opt_update(i, grad(loss)(params, batch), opt_state)

  _, init_params = init_random_params(rng, (-1, 28 * 28))
  opt_state = opt_init(init_params)
  itercount = itertools.count()

  print("\nStarting training...")
  for epoch in range(num_epochs):
    start_time = time.time()
    for _ in range(num_batches):
      opt_state = update(next(itercount), opt_state, next(batches))
    epoch_time = time.time() - start_time

    params = optimizers.get_params(opt_state)
    train_acc = accuracy(params, (train_images, train_labels))
    test_acc = accuracy(params, (test_images, test_labels))
    print("Epoch {} in {:0.2f} sec".format(epoch, epoch_time))
    print("Training set accuracy {}".format(train_acc))
    print("Test set accuracy {}".format(test_acc))
